docs: def calc_abc_interval(self,
                            conf_percentage,
                            init_vals,
                            epsilon=0.001,
                            **fit_kwargs):
          """
          Calculates Approximate Bootstrap Confidence Intervals for one's model.
  
          Parameters
          ----------
          conf_percentage : scalar in the interval (0.0, 100.0).
              Denotes the confidence-level for the returned endpoints. For
              instance, to calculate a 95% confidence interval, pass `95`.
          init_vals : 1D ndarray.
              The initial values used to estimate the one's choice model.
          epsilon : positive float, optional.
              Should denote the 'very small' value being used to calculate the
              desired finite difference approximations to the various influence
              functions. Should be close to zero.
              Default == sys.float_info.epsilon.
          fit_kwargs : additional keyword arguments, optional.
              Should contain any additional kwargs used to alter the default
              behavior of `model_obj.fit_mle` and thereby enforce conformity with
              how the MLE was obtained. Will be passed directly to
              `model_obj.fit_mle`.
  
          Returns
          -------
          None. Will store the ABC intervals as `self.abc_interval`.
          """
          print("Calculating Approximate Bootstrap Confidence (ABC) Intervals")
          print(time.strftime("%a %m-%d-%Y %I:%M%p"))
          sys.stdout.flush()
          # Get the alpha % that corresponds to the given confidence percentage.
          alpha = bc.get_alpha_from_conf_percentage(conf_percentage)
          # Create the column names for the dataframe of confidence intervals
          single_column_names =\
              ['{:.3g}%'.format(alpha / 2.0),
               '{:.3g}%'.format(100 - alpha / 2.0)]
          # Calculate the ABC confidence intervals
          conf_intervals =\
              abc.calc_abc_interval(self.model_obj,
                                    self.mle_params.values,
                                    init_vals,
                                    conf_percentage,
                                    epsilon=epsilon,
                                    **fit_kwargs)
          # Store the ABC confidence intervals
          self.abc_interval = pd.DataFrame(conf_intervals.T,
                                           index=self.mle_params.index,
                                           columns=single_column_names)
          return None